Scalable Communication-aware Task Mapping Algorithms for Interconnected Multicore Systems

Communication-aware task mapping algorithms, which map parallel tasks onto processing nodes according to the communication patterns of applications, are essential to reduce the communication time in modern high performance computing. In this paper, we design algorithms specifically for interconnected multicore systems, whose architectural property, namely small number of cores per node, large number of nodes, and large performance gap between the communication within a multicore and among multicores, had brought new challenges and opportunities to the mapping problem. Let k be the number of cores per multicore and n be the number of tasks. We consider the practical case that k ≪ n for k = 2, 4, and 6. The designed algorithms are optimal for the mapping measurement, called Maximum Interconnective Message Size (MIMS), and of time complexity merely O(m log m) for m communication pairs. Thus, they are highly scalable for large applications. We had experimented the algorithms on the IBM Blue Gene/P system for two synthetic benchmarks and two applications. The results show good communication performance improvement.